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pip install --upgrade wordcloud pillow
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from sklearn.model_selection import train_test_split
from sklearn.linear_model import PassiveAggressiveRegressor
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
import plotly.offline as pyo
data = pd.read_csv("Instagram data.csv", encoding = 'latin1')
print(data.head())
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#Before starting everything, let’s have a look at whether this dataset contains any null values or not:
data.isnull().sum()
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#So it has a null value in every column. Let’s drop all these null values and move further:
data = data.dropna()
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#Let’s have a look at the insights of the columns to understand the data type of all the columns:
data.info()
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#Analyzing Instagram Reacplt.figure(figsize=(10, 8))
plt.style.use('fivethirtyeight')
plt.title("Distribution of Impressions From Home")
sns.distplot(data['From Home'])
plt.show()
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#The impressions I get from the home section on Instagram shows how much my posts reach my followers. Looking at the impressions
#from home, I can say it’s hard to reach all my followers daily. Now let’s have a look at the distribution of the impressions
#I received from hashtags:
plt.figure(figsize=(10, 8))
plt.title("Distribution of Impressions From Hashtags")
sns.distplot(data['From Hashtags'])
plt.show()
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#Hashtags are tools we use to categorize our posts on Instagram so that we can reach more people based on the kind of content we
#are creating. Looking at hashtag impressions shows that not all posts can be reached using hashtags, but many new users can be
#reached from hashtags. Now let’s have a look at the distribution of impressions I have received from the explore section of Instagram:
plt.figure(figsize=(10, 8))
plt.title("Distribution of Impressions From Explore")
sns.distplot(data['From Explore'])
plt.show()
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#The explore section of Instagram is the recommendation system of Instagram. It recommends posts to the users based on their
#preferences and interests. By looking at the impressions I have received from the explore section, I can say that Instagram
#does not recommend our posts much to the users. Some posts have received a good reach from the explore section, but it’s still
#very low compared to the reach I receive from hashtags.
#Now let’s have a look at the percentage of impressions I get from various sources on Instagram:
home = data["From Home"].sum()
hashtags = data["From Hashtags"].sum()
explore = data["From Explore"].sum()
other = data["From Other"].sum()
labels = ['From Home','From Hashtags','From Explore','Other']
values = [home, hashtags, explore, other]
fig = px.pie(data, values=values, names=labels,
title='Impressions on Instagram Posts From Various Sources', hole=0.5)
pyo.iplot(fig)
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#So the above donut plot shows that almost 50 per cent of the reach is from my followers, 38.1 per cent is from hashtags,
#9.14 per cent is from the explore section, and 3.01 per cent is from other sources.
#Analyzing Content Now let’s analyze the content of my Instagram posts. The dataset has two columns, namely caption and
#hashtags, which will help us understand the kind of content I post on Instagram.
#Let’s create a wordcloud of the caption column to look at the most used words in the caption of my Instagram posts:
text = " ".join(i for i in data.Caption)
stopwords = set(STOPWORDS)
wordcloud = WordCloud(stopwords=stopwords, background_color="white").generate(text)
plt.style.use('classic')
plt.figure( figsize=(12,10))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()
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#Now let’s create a wordcloud of the hashtags column to look at the most used hashtags in my Instagram posts:
text = " ".join(i for i in data.Hashtags)
stopwords = set(STOPWORDS)
wordcloud = WordCloud(stopwords=stopwords, background_color="white").generate(text)
plt.figure( figsize=(12,10))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()
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#Analyzing Relationships Now let’s analyze relationships to find the most important factors of our Instagram reach. It will
#also help us in understanding how the Instagram algorithm works.
#Let’s have a look at the relationship between the number of likes and the number of impressions on my Instagram posts:
figure = px.scatter(data_frame = data, x="Impressions",
y="Likes", size="Likes", trendline="ols",
title = "Relationship Between Likes and Impressions")
pyo.iplot(figure)
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#There is a linear relationship between the number of likes and the reach I got on Instagram. Now let’s see the relationship
#between the number of comments and the number of impressions on my Instagram posts:
figure = px.scatter(data_frame = data, x="Impressions",
y="Comments", size="Comments", trendline="ols",
title = "Relationship Between Comments and Total Impressions")
pyo.iplot(figure)
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#It looks like the number of comments we get on a post doesn’t affect its reach. Now let’s have a look at the relationship
#between the number of shares and the number of impressions:
figure = px.scatter(data_frame = data, x="Impressions",
y="Shares", size="Shares", trendline="ols",
title = "Relationship Between Shares and Total Impressions")
pyo.iplot(figure)
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#A more number of shares will result in a higher reach, but shares don’t affect the reach of a post as much as likes do.
#Now let’s have a look at the relationship between the number of saves and the number of impressions:
figure = px.scatter(data_frame = data, x="Impressions",
y="Saves", size="Saves", trendline="ols",
title = "Relationship Between Post Saves and Total Impressions")
pyo.iplot(figure)
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#Analyzing Conversion Rate In Instagram, conversation rate means how many followers you are getting from the number of profile
#visits from a post. The formula that you can use to calculate conversion rate is (Follows/Profile Visits) * 100. Now let’s have a look at the conversation rate of my Instagram account:
conversion_rate = (data["Follows"].sum() / data["Profile Visits"].sum()) * 100
print(conversion_rate)
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#So the conversation rate of my Instagram account is 31% which sounds like a very good conversation rate. Let’s have a look at
#the relationship between the total profile visits and the number of followers gained from all profile visits:
figure = px.scatter(data_frame = data, x="Profile Visits",
y="Follows", size="Follows", trendline="ols",
title = "Relationship Between Profile Visits and Followers Gained")
pyo.iplot(figure)
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#The relationship between profile visits and followers gained is also linear.
#Instagram Reach Prediction Model Now in this section, I will train a machine learning model to predict the reach of an
#Instagram post. Let’s split the data into training and test sets before training the model:
x = np.array(data[['Likes', 'Saves', 'Comments', 'Shares',
'Profile Visits', 'Follows']])
y = np.array(data["Impressions"])
xtrain, xtest, ytrain, ytest = train_test_split(x, y,
test_size=0.2,
random_state=42)
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#Now here’s is how we can train a machine learning model to predict the reach of an Instagram post using Python:
model = PassiveAggressiveRegressor()
model.fit(xtrain, ytrain)
model.score(xtest, ytest)
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#Now let’s predict the reach of an Instagram post by giving inputs to the machine learning model:
# Features = [['Likes','Saves', 'Comments', 'Shares', 'Profile Visits', 'Follows']]
features = np.array([[282.0, 233.0, 4.0, 9.0, 165.0, 54.0]])
model.predict(features)
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#So this is how you can analyze and predict the reach of Instagram posts with machine learning using Python. If a content creator wants to do well on Instagram in a long run, they have to look at the data of their Instagram reach. That is where the use of Data Science in social media comes in. I hope you liked this article on the task of Instagram Reach Analysis using Python. Feel free to ask valuable questions in the comments section below.